In this work, we formulate a novel problem in the context
of resilient consensus for multi-agent systems,
where the agents may fall in infectious statuses
depending on the susceptible-infected-recovered (SIR) epidemic dynamics in the environment.
Infected agents will take unexpected behaviors in their
values and agents free from the disease should avoid
using such state values when updating their own ones.
While we follow the line of research on fault tolerant consensus
algorithms (e.g., [1]}), the main difference
is that the number of fault agents is time varying.
This information may be partially known from high-level
policy makers who attempts to estimate the epidemic condition
and make announcement about it in real time.
